Three-Dimensional Imaging of the Mouse Neurovasculature with Magnetic Resonance Microscopy

Knowledge of the three-dimensional (3D) architecture of blood vessels in the brain is crucial because the progression of various neuropathologies ranging from Alzheimer's disease to brain tumors involves anomalous blood vessels. The challenges in obtaining such data from patients, in conjunction with development of mouse models of neuropathology, have made the murine brain indispensable for investigating disease induced neurovascular changes. Here we describe a novel method for “whole brain” 3D mapping of murine neurovasculature using magnetic resonance microscopy (μMRI). This approach preserves the vascular and white matter tract architecture, and can be combined with complementary MRI contrast mechanisms such as diffusion tensor imaging (DTI) to examine the interplay between the vasculature and white matter reorganization that often characterizes neuropathologies. Following validation with micro computed tomography (μCT) and optical microscopy, we demonstrate the utility of this method by: (i) combined 3D imaging of angiogenesis and white matter reorganization in both, invasive and non-invasive brain tumor models; (ii) characterizing the morphological heterogeneity of the vascular phenotype in the murine brain; and (iii) conducting “multi-scale” imaging of brain tumor angiogenesis, wherein we directly compared in vivo MRI blood volume measurements with ex vivo vasculature data

New MR imaging techniques in epilepsy

This thesis is concerned with the application of three magnetic resonance (MR) techniques in epilepsy: i.) Fluid attenuated inversion recovery prepared (FLAIR) imaging, ii.) diffusion imaging including diffusion tensor imaging (DTI) and iii.) serial and high resolution imaging of the hippocampus. I assessed the clinical value of fast FLAIR in epilepsy in a study involving 128 patients and of 3D FLAIR in a study involving 10 patients. The conspicuity of neocortical lesions and hippocampal sclerosis was increased. New lesions were detected in 5% of patients. The extent of low grade tumours was best assessed on 3D fast FLAIR images. Fast FLAIR was inferior to standard MR techniques for identifying and heterotopia. I applied newly developed, experimental diffusion imaging techniques. In eight studies using different diffusion imaging techniques involving a total of 50 patients and 54 control subjects I investigated the mobility of water molecules in the human epileptic brain in vivo. I used spin echo diffusion imaging in two studies, echo planar imaging (EPI) based DTI in four studies and EPI diffusion imaging in a patient during focal status epilepticus. Finally, in a preliminary study I attempted to use EPI diffusion imaging as a contrast to visualise transient changes associated with frequent lateralizing spikes. Our findings were: i.) diffusion is increased in hippocampal sclerosis suggesting a loss of structural organization and expansion of the extracellular space, ii.) displaying the directionality (anisotropy) of diffusion is superior to standard imaging to visualise tracts, iii.) anisotropy is reduced in the pyramidal tract in patients with hemiparesis and iv.) in the optic radiation in patients with hemianopia after temporal lobectomy suggesting wallerian degeneration, v.) both developmental and acquired structural abnormalities have a lower anisotropy than normal white matter, vi.) diffusion abnormalities in blunt head trauma are widespread and may include regions which are normal on standard imaging, indicating micro structural damage suggestive of diffuse axonal injury, vii.) focal status epilepticus can be associated with a reduced difflision in the affected cortex, viii.) diffusion imaging may be useful as a contrast for event-related (spike triggered) functional MR imaging. With serial MRI I demonstrated hippocampal volume loss in a patient after generalized status epilepticus and with high resolution imaging of an anatomical specimen and a control subject I showed hippocampal layers on MR images. The results presented in this thesis emphasised the flexibility of MR imaging and its ability to demonstrate abnormalities in vivo. FLAIR imaging is now part of the clinical work up of patients with epilepsy. Diffusion imaging has been shown to be superior to standard imaging to visualise tracts which has far-reaching implications for neurological applications. Diffusion imaging also provides an exciting window to study cerebral micro structure in vivo. Serial imaging allows for the first time the visualisation of temporal changes and high resolution imaging has the prospect of demonstrating hippocampal layers in vivo. MR imaging is a constantly progressing technique. It is hoped that this thesis will help to formulate hypotheses for new MR experiments to study the relationship of dysfunction and structural abnormalities

Computational methods to predict and enhance decision-making with biomedical data.

The proposed research applies machine learning techniques to healthcare applications. The core ideas were using intelligent techniques to find automatic methods to analyze healthcare applications. Different classification and feature extraction techniques on various clinical datasets are applied. The datasets include: brain MR images, breathing curves from vessels around tumor cells during in time, breathing curves extracted from patients with successful or rejected lung transplants, and lung cancer patients diagnosed in US from in 2004-2009 extracted from SEER database. The novel idea on brain MR images segmentation is to develop a multi-scale technique to segment blood vessel tissues from similar tissues in the brain. By analyzing the vascularization of the cancer tissue during time and the behavior of vessels (arteries and veins provided in time), a new feature extraction technique developed and classification techniques was used to rank the vascularization of each tumor type. Lung transplantation is a critical surgery for which predicting the acceptance or rejection of the transplant would be very important. A review of classification techniques on the SEER database was developed to analyze the survival rates of lung cancer patients, and the best feature vector that can be used to predict the most similar patients are analyzed

Large-scale neuroimaging in Alzheimer’s disease and normal aging

Large-scale neuroimaging data is becoming increasingly available, providing a rich data source with which to study neurological conditions. In this thesis, I demonstrate the utility of large-scale neuroimaging as it applies to Alzheimer’s disease (AD) and normal aging, using univariate parametric mapping, regional analysis, and advanced machine learning. Specifically, this thesis covers: 1) validation and extension of prior studies using large-scale datasets; 2) AD diagnosis and normal aging evaluation empowered by large-scale datasets and advanced deep learning algorithms; 3) enhancement of cerebral blood volume (CBV) fMRI utility with retrospective CBV-fMRI technique. First, I demonstrated the utility of large-scale datasets for validating and extending prior studies using univariate analytics. I presented a study localizing AD-vulnerable regions more reliably and with better anatomical resolution using data from more than 350 subjects. Following a similar approach, I investigated the structural characteristics of healthy APOE ε4 homozygous subjects screened from a large-scale community-based study. To study the neuroimaging signatures of normal aging, we performed a large-scale joint CBV-fMRI and structural MRI study covering age 20-70s, and a structural MRI study of normal aging covering the full age-span, with the elder group screened from a large-scale clinic-based study ensuring no evidence of AD using both longitudinal follow-up and cerebrospinal fluid (CSF) biomarkers evidences. Second, I performed deep learning neuroimaging studies for AD diagnosis and normal aging evaluation, and investigated the regionality associated with each task. I developed an AD diagnosis method using a 3D convolutional neural network model trained and evaluated on ~4,600 structural MRI scans and further investigated a series of novel regionality analyses. I further extensively studied the utility of the structural MRI summary measure derived from the deep learning model in prodromal AD detection. This study constitutes a general analytic framework, which was followed to evaluate normal aging by performing deep learning-based age estimation in cognitively normal population using more than 6,000 scans. The deep learning neuroimaging models classified AD and estimated age with high accuracy, and also revealed regional patterns conforming to neuropathophysiology. The deep learning derived MRI measure demonstrated potential clinical utility, outperforming other AD pathology measures and biomarkers. In addition, I explored the utility of deep learning on positron emission tomography (PET) data for AD diagnosis and regionality analyses, further demonstrating the broad utility and generalizability of the method. Finally, I introduced a technique enabling CBV generation retrospectively from clinical contrast-enhanced scans. The derivation of meaningful functional measures from such clinical scans is only possible through calibration to a reference, which was built from the largest collection of research CBV-fMRI scans from our lab. This method was validated in an epilepsy study and demonstrated the potential to enhance the utility of CBV-fMRI by enriching the CBV-fMRI dataset. This technique is also applicable to AD and normal aging studies, and potentially enables deep learning based analytic approaches applied on CBV-fMRI with similar pipelines used in structural MRI. Collectively, this thesis demonstrates how mechanistic and diagnostic information on brain disorders can be extracted from large-scale neuroimaging data, using both classical statistical methods and advanced machine learning

Neurocognitive patterns and progression of mild cognitive impairment with Lewy bodies or Alzheimer’s disease

PhD ThesisMild cognitive impairment is a heterogeneous condition; while typically viewed as the transition stage between healthy cognitive function and dementia, the cognitive and clinical patterns of this condition vary, as do its prognosis. The two most common neurodegenerative dementias, dementia with Lewy bodies and Alzheimer’s disease, differ in their clinical features, patterns of cognitive impairment, and prognosis. It is not known whether the respective mild cognitive impairment stages preceding onset of dementia also differ in these characteristics. Two cohorts of people with recent mild cognitive impairment diagnosis were assessed, undergoing annual review of cognition, diagnosis and presence of clinical features of Lewy body disease by an expert panel of old age psychiatrists, and repeated imaging, to reach a consensus diagnosis of either mild cognitive impairment due to Alzheimer’s disease, or with Lewy bodies, in line with current consensus criteria for these. Making use of annually repeated cognitive assessment and clinical diagnostic information, the longitudinal progression of these two conditions was characterised with flexible statistical methods, using the first cohort for model development, and the second for validation: different trajectories of decline in specific cognitive domains were observed in the diagnostic groups, reflecting typical patterns of impairment in their respective dementia syndromes. Mild cognitive impairment with Lewy bodies was also observed to have a worse prognosis, in keeping with its dementia stage, with a greater risk of progressive cognitive decline, and faster onset of dementia. Individuals with neuropsychiatric symptoms (cognitive fluctuations and visual hallucinations) were also at more risk of decline than those with slower-developing features (REM sleep behaviour disorder or parkinsonism) or those with Alzheimer’s disease. Mild cognitive impairment with Lewy bodies may therefore feature early cognitive, clinical, and prognostic differences from Alzheimer’s disease, reflecting its eventual dementia syndrome

Neuromyelitis optica (NMO): identifying imaging markers to improve diagnosis and inform therapeutic innovation

The main objective of the thesis is to use advanced MRI techniques to look for biomarkers that separate neuromyelitis optica spectrum disorders (NMOSD) from MS to improve diagnosis. NMOSD, a severe inflammatory disease which causes demyelination of the central nervous system, is characterised by optic neuritis (ON) and acute myelitis. Because of similarities with MS, NMOSD is not always correctly diagnosed at onset. As it is both more aggressive and faster progressing than MS, an early accurate diagnosis is crucial. For this thesis, three different MRI techniques were used, together with clinical assessments, to gain a better understanding of the differences between the two diseases. The first was neurite orientation dispersion and density imaging (NODDI), a diffusion MR technique used to analyse the microstructure of dendrites and axons. When applied to a single-shell dataset of RRMS patients, it was shown to detect more regions of diffusion abnormalities than FA maps. The second technique used is phase-sensitive inversion recovery (PSIR), to look for grey matter lesions. This first application to NMOSD patients led to the detection of grey matter lesions in nearly 50% of this group, as well as showing differences in leucocortical and juxtacortical lesions between NMOSD and MS, with juxtacortical lesions emerging as potential markers to differentiate between these diseases. The final part applies magnetisation transfer ratio (MTR) to the optic nerve to assess myelin integrity in both MS and NMOSD patients, together with optic coherence tomography (OCT) for the macula the retinal nerve fibre layer and visual assessments. Significant differences in MTR and OCT values were found in MS and NMOSD patients with ON compared to healthy controls (HC). Significant differences were found between the unaffected nerve of NMOSD patients and HC, but not between groups for either MTR values (after correction for age) and OCT measurements

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world

Pharmacological fMRI; a clinical exploration

Dit proefschrift beschrijft de resultaten van een verkennend onderzoek naar een nieuwe techniek die gebruikt kan worden om de effecten van geneesmiddelen op hersenaktiviteit af te beelden: pharmacologische functionele magnetic resonance imaging (farmacologische fMRI of phMRI). Met behulp van deze techniek werden de effecten onderzocht van drie verschillende medicijnen (de bètablokker propranolol, de selectieve oestrogeen-receptor modulator (SERM) raloxifene en de cholinesteraseremmer galantamine) op hersenaktiviteit van respectievelijk gezonde jongere en oudere controles, en patiënten met geheugenklachten. Aan de hand van de resultaten van dit onderzoek werd nagegaan in hoeverre phMRI toepasbaar zou kunnen zijn in een klinische context. De MRI scanner is al bijna 30 jaar een vast onderdeel van het instrumentarium van radiologen. MRI is een volledig non-invasieve scantechniek die artsen in staat stelt om haarscherpe, driedimensionale afbeeldingen te maken van de verschillende organen van levende patiënten en de eventuele ziekteprocessen die zich daarin afspelen. fMRI is een meer recente toepassing van MRI, waarbij de wisselende magnetische eigenschappen van de rode bloedkleurstof ‘hemoglobine’ worden benut om zwart-wit contrast te geven aan MR plaatjes (Jezzard et al., 2001). Afhankelijk van de mate waarin hersengebieden aktief zijn wordt een sterker of minder sterk bloed-oxygenatie afhankelijk (BOLD) MR signaal gemeten. De meeste fMRI studies vergelijken de gemiddelde signaalintensiteiten van hersengebieden tussen actieve en een minder actieve condities van een bepaalde taak, die tijdens het scannen wordt uitgevoerd. Dit levert driedimensionaal plaatjes op van het hele brein, waarin de gemiddelde intensiteitsverschillen tussen twee taak-condities zijn af te lezen (“contrast plaatjes”). phMRI is een verdere nuancering van fMRI, waarbij contrastplaatjes worden onderzocht op toenames of afnames van intensiteitsverschillen als gevolg van blootstelling aan een bepaalde farmacologische stof. Aangezien de meeste psychotrope geneesmiddelen aangrijpen in specifieke neurotransmittersystemen zou phMRI gebruikt kunnen worden om de toestand van deze systemen te onderzoeken in de gezonde en de zieke situatie. phMRI zou dus eventueel als klinisch-diagnostisch instrument kunnen worden gebruikt. Er is echter nog maar weinig onderzoek gedaan naar de klinische waarde van phMRI (Honey and Bullmore, 2004). Dit proefschrift had tot doel dit terrein verder te verkennen. Patiënten en proefpersonen werden gescand met behulp van fMRI tijdens het uitvoeren van speciaal voor dit onderzoek ontwikkelde geheugentestjes (paradigmata). Hierdoor waren we in staat om hersengebieden zichtbaar te maken die betrokken zijn bij verschillende aspecten van geheugenfunctie. De effecten van geneesmiddelen werden onderzocht op hersenfunctie zoals die werd opgeroepen door bovengenoemde taken. We gebruikten achtereenvolgens een ‘face-encoding’ taak (onthouden van onbekende menselijke gezichten (Small et al., 1999)), een ‘face-recognition’ taak (herkennen van eerder getoonde gezichten), en een n-letter back werkgeheugen taak (tijdelijke opslag van nieuwe informatie (Owen et al., 2005)). Verder werd in samenwerking met Dr. A.H. van Stegeren and Prof. Dr. W.TA.M. Everaerd van de afdeling Klinische en Experimentele Psychologie van de Universiteit van Amsterdam het International Affective Picture System (IAPS) (Lang and Bradley, 1997), aangepast voor gebruik in de MR scanner. Deze taak had tot doel hersengebieden te aktiveren die betrokken zijn bij de opslag van emotioneel negatief geladen (onprettige) informatie. fMRI analyses werden gedaan met de fMRI expert analysis tool (FEAT) uit het data-analyse pakket fMRIB software library (FSL) (Smith et al., 2004). Ons onderzoek leverde de volgende bevindingen op: 1. Effecten van verschillende geneesmiddelen op hersenfunctie bij mensen kunnen worden gemeten met behulp van fMRI, maar zijn vrij klein. 2. Onze bevindingen sluiten aan bij die van ander fMRI onderzoek waaruit blijkt dat de effecten van geneesmiddelen op hersenfunctie regio-specifiek en proces-specifiek kunnen zijn (Honey and Bullmore, 2004). 3. De effecten van geneesmiddelen op hersenfunctie zijn tevens geslachts- en ziekte-(stadium)-specifiek. 4. Deze effecten zijn afhankelijk van de duur van blootstelling aan het farmacologische agens. 5. In het geval van cholinerge stimulatie leidde een éénmalige stootdosis galantamine al tot regio- proces- en ziektespecifieke effecten. 6. We hebben een methode ontwikkeld om de effecten van geneesmiddelen op hersenfunctie tijdens geheugenfunctie te onderzoeken in relatie tot gedragsveranderingen en specifieke hersenprocessen. 7. Indien grote groepen patiënten geïncludeerd worden zijn uitgebreide studies naar de klinische (vroegdiagnostische en predictieve) waarde van de gemeten effecten zeer goed mogelijk. phMRI is voorlopig nog een onderzoeksinstrument. Vanuit klinisch oogpunt kunnen de bevindingen van phMRI studies echter erg interessant zijn. Studies van de regio- en proces-specificiteit van geneesmiddelen kunnen helpen bij het ontwikkelen van geneesmiddelen die meer gericht inwerken op specifieke hersenprocessen. Dit zou mogelijk de effectiviteit van geneesmiddelen kunnen verhogen en het aantal bijwerkingen kunnen verminderen. phMRI kan verder worden ingezet om het werkingsmechanisme van psychotrope geneesmiddelen te onderzoeken. phMRI provokatietests laten bovendien zien dat de onmiddellijke effecten van geneesmiddelen een vroeg-diagnostische waarde kunnen hebben, en misschien zelfs het succes van farmacotherapie kunnen voorspellen (Fu et al., 2004). Toekomstig onderzoek zal moeten uitwijzen in hoeverre phMRI in staat is om klinisch relevante functionele biomarkers aan te tonen in individuele patiënten. Ondertussen kunnen groepsstudies al resultaten leveren die van klinisch belang zouden kunnen zijn voor individuele patiënten. Met phMRI provokatietests kunnen relaties worden gelegd tussen bepaalde klinische verschijnselen en een afwijkende reactiviteit van centrale neurotransmittersystemen. Zijn zulke relaties eenmaal gelegd, dan kunnen patiënten die zich presenteren met de bewuste symptomen of afwijkingen worden behandeld met een gerichte farmacotherapie. De ontwikkelingen in de methodologie van fMRI staan ondertussen niet stil. Er is inmiddels een arsenaal aan nieuwe analysemethoden beschikbaar gekomen, die van belang zouden kunnen zijn voor toekomstig phMRI onderzoek. Ook al staat farmacologische fMRI nog in de kinderschoenen, er wordt met grote stappen vooruitgang geboekt. Het is dus waarschijnlijk dat phMRI, in combinatie met andere technieken, een belangrijk bijdrage zal gaan leveren aan de zoektocht naar biologische markers met een bruikbare klinische waarde. Reference List Fu CHY, Williams SCR, Cleare AJ, Brammer MJ, Walsh ND, Kim J, Andrew CM, Pich EM, Williams PM, Reed LJ, Mitterschiffthaler MT, Suckling J, Bullmore ET. 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